Text explaining why the neural network representation of common features (typically with weighted proportionality to their occurrence) does not meet the definition of a mathematical average. Does it not favor common response patterns?

Hmm. I’m not really sure why anyone would write such a text. There is no “weighted proportionality” (or pathways). Is this a common conception?

You don’t need it to be an average of the real world to be an average. I can calculate as many average values as I want from entirely fictional worlds. It’s still a type of model which favors what it sees often over what it sees rarely. That’s a form of probability embedded, corresponding to a form of average.

I guess you picked up on the fact that transformers output a probability distribution. I don’t think anyone calls those an average, though you could have an average distribution. Come to think of it, before you use that to pick the next token, you usually mess with it a little to make it more or less “creative”. That’s certainly no longer an average.

You can see a neural net as a kind of regression analysis. I don’t think I have ever heard someone calling that a kind of average, though. I’m also skeptical if you can see a transformer as a regression but I don’t know this stuff well enough. When you train on some data more often than on other data, that is not how you would do a regression. Certainly, once you start RLHF training, you have left regression territory for good.

The GPTisms might be because they are overrepresented in the finetuning data. It might also be from the RLHF and/or brought out by the system prompt.